Time Series Classification by Boosting Interval Based literals

Time Series Classification by Boosting Interval Based Literals

A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of predicates are used: i) relative predicates, such as “increases” and “stays”, and ii) region predicates, such as “always” and “sometime”, which opera...

Asociación Española Para La Inteligencia Artificial España Time Series Classification by Boosting Interval Based Literals *

A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of predicates are used: i) relative predicates, such as “increases” and “stays”, and ii) region predicates, such as “always” and “sometime”, which opera...

Boosting interval based literals

A supervised classification method for time series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of predicates are used: i) relative predicates, such as “increases” and “stays”, and ii) region predicates, such as “always” and “sometime”, which operate o...

Applying Boosting to Similarity Literals for Time Series Classification

Boosting Interval-Based Literals: Variable Length and Early Classification

In previous works, a system for supervised time series classification has been presented. It is based on boosting very simple classifiers: only one literal. The used predicates are based on temporal intervals. There are two types of predicates: i) relative predicates, such as “increases” and “stays”, and ii) region predicates, such as “always” and “sometime”, which operate ver regions in the do...